Prior to implementing a design, especially in a production mode, such as for machinery including motors or pumps used with artificial lift technology and systems, manufacturers often like to test assumptions and engineering decisions about that machine. For some machinery, a testing phase can become expensive and involve prototypes and project scheduling delays. Exhaustive testing, e.g. using multiple scenarios to investigate response of the machine to the scenario, can be prohibitively costly or time consuming or both.
Newer modeling techniques can often save time and cost, e.g. a model of a wind-tunnel session can often be less costly—and nearly or as accurate—as using an actual wind tunnel. Modeling of behavior, such as neural networks, can adapt to changes in supplied environmental variables and can further be refined using real world data, leading to decreased time to market and decreased time to test a new design. Such modeling has been used and/or suggested for such applications as replacement and/or augmentation of flow performance features in a wind tunnel and other flow modeling applications.